This is the definitive guide to data lake on AWS. Whether you are a non-technical Director, CTO, or CEO you can learn everything you need to know about Data Lakes – at a business overview level – right here.

Overview

What is a data lake

A data lake is a smart form of data storage that can hold any given type of business data; taken directly from its source. 

Data Lakes stores structured and unstructured data in one centralised location without limitations on scaling. Data Lakes are data-agnostic – they don’t care what type of data they store, unlike a data warehouse that will only accept data that has been preformatted in a specific way to fit into tables.

Data lakes supersede the capabilities of relational databases which perform best with a single source of structured data. They are not limited by their scale-like relational databases. The cost of storing data in its raw form is affordable and quick in a Data Lake whereas data warehouses must process and prepare the data for storage and analysis, so it takes time and money. Data Lakes are an affordable storage solution and source of valuable insights for companies with large volumes of business data. 

Why use a Data Lake

Forward-thinking businesses use Data Lakes to harness their big data and outperform their competition. This is because, through machine learning and smart analysis, Data Lakes can generate highly-valuable insights from new data types like log files, clickstreams, social media, and IoT devices. Amazon AWS has ‘plug in and play’ machine learning services like Amazon Rekognition and Amazon Comprehend, to help organisations gain valuable insights from their Data Lakes.

Business insights commonly generated from AWS Data Lakes include; proactive maintenance alerting, productivity-boosting and operational efficiency gains, improved research and development decision making, knowing where to concentrate efforts for customer retention, and more. All of which can give a company competitive advantages and increase organic growth revenue.

With a Data Lake, it is possible to call up and examine any piece of data at a micro-level. No business information needs to go to waste when using an AWS Data Lake solution because Data Lakes can store and support all types of data in its raw form. Data Lakes are being used because they allow businesses to gain advantageous insights from weblogs and clickstreaming as well as transactions, social media and the internet of things.

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